# Many many variables are included in the ACS. The ACS has 1 and 5 year estimates. Use the following code to see what variables are available:
# Set a year of interest
this.year = 2021
# This looks at the 5 year estimates
# You can also do "acs1"
vars <- load_variables(year = 2021,
dataset = "acs5",
cache = TRUE)
# There are 27886 possible variables
dim(vars)[1] 27886 4## Names for variable types
# Gives five year estimates
#OR
orDem <- get_acs(geography = "tract", year=this.year,
state = "OR",
geometry = TRUE,
variables = c(popululation = "B02001_001",
median.gross.rent = "B25064_001",
median.household.income = "B19013_001",
rent.burden = "B25071_001",
white = "B03002_003",
af.am = "B03002_004",
hispanic = "B03002_012",
am.ind = "B03002_005",
asian = "B03002_006",
nh.pi = "B03002_007",
multiple = "B03002_009",
other = "B03002_008"))
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|======================================================================| 100%head(orDem)Simple feature collection with 6 features and 5 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -122.9903 ymin: 44.9539 xmax: -122.9767 ymax: 44.96986
Geodetic CRS: NAD83
GEOID NAME variable estimate
1 41047001607 Census Tract 16.07, Marion County, Oregon popululation 4442
2 41047001607 Census Tract 16.07, Marion County, Oregon white 1359
3 41047001607 Census Tract 16.07, Marion County, Oregon af.am 0
4 41047001607 Census Tract 16.07, Marion County, Oregon am.ind 33
5 41047001607 Census Tract 16.07, Marion County, Oregon asian 214
6 41047001607 Census Tract 16.07, Marion County, Oregon nh.pi 982
moe geometry
1 868 MULTIPOLYGON (((-122.9903 4...
2 600 MULTIPOLYGON (((-122.9903 4...
3 13 MULTIPOLYGON (((-122.9903 4...
4 56 MULTIPOLYGON (((-122.9903 4...
5 182 MULTIPOLYGON (((-122.9903 4...
6 465 MULTIPOLYGON (((-122.9903 4...## We’re going to need to do a little data wrangling so that we have a tidydata format to spread the column named ‘variable’.
orPct<-as.data.frame(orDem)[,c(1,3:4)]%>%
spread(variable, estimate)%>%
mutate(checkTot = white+af.am+hispanic+am.ind+ # looks good!
asian+nh.pi+multiple+other)%>%
mutate(pct.white = white/checkTot,
pct.af.am = af.am/checkTot,
pct.hispanic = hispanic/checkTot,
pct.am.ind = am.ind/checkTot,
pct.asian = asian/checkTot,
pct.nh.pi = nh.pi/checkTot,
pct.multiple = multiple/checkTot,
pct.other = other/checkTot,
year = this.year)
head(orPct) GEOID af.am am.ind asian hispanic median.gross.rent
1 41001950100 26 10 3 108 844
2 41001950200 9 9 9 106 706
3 41001950300 94 77 3 15 625
4 41001950400 15 0 27 223 787
5 41001950500 37 56 0 239 643
6 41001950600 6 83 16 105 731
median.household.income multiple nh.pi other popululation rent.burden white
1 51211 84 13 0 2579 27.0 2335
2 45297 102 5 9 3332 34.8 3083
3 42237 41 11 0 2845 35.4 2604
4 52843 0 0 0 2829 30.6 2564
5 54318 123 0 0 2579 29.5 2124
6 48000 53 3 0 2375 26.1 2109
checkTot pct.white pct.af.am pct.hispanic pct.am.ind pct.asian
1 2579 0.9053897 0.010081427 0.041876696 0.003877472 0.001163242
2 3332 0.9252701 0.002701080 0.031812725 0.002701080 0.002701080
3 2845 0.9152900 0.033040422 0.005272408 0.027065026 0.001054482
4 2829 0.9063273 0.005302227 0.078826440 0.000000000 0.009544008
5 2579 0.8235750 0.014346646 0.092671578 0.021713843 0.000000000
6 2375 0.8880000 0.002526316 0.044210526 0.034947368 0.006736842
pct.nh.pi pct.multiple pct.other year
1 0.005040713 0.03257076 0.00000000 2021
2 0.001500600 0.03061224 0.00270108 2021
3 0.003866432 0.01441125 0.00000000 2021
4 0.000000000 0.00000000 0.00000000 2021
5 0.000000000 0.04769290 0.00000000 2021
6 0.001263158 0.02231579 0.00000000 2021# Set a year first
this.year = 2021
### BASE PLOT EXAMPLE
### Washington
or_tracts <- tracts(state = 'OR',
cb = T, year = this.year)
# This is the structure of spatial data
head(or_tracts)Simple feature collection with 6 features and 13 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -122.9903 ymin: 44.88572 xmax: -118.7871 ymax: 45.7816
Geodetic CRS: NAD83
STATEFP COUNTYFP TRACTCE AFFGEOID GEOID NAME
1 41 047 001607 1400000US41047001607 41047001607 16.07
2 41 005 980000 1400000US41005980000 41005980000 9800
3 41 051 001301 1400000US41051001301 41051001301 13.01
4 41 067 031516 1400000US41067031516 41067031516 315.16
5 41 059 950500 1400000US41059950500 41059950500 9505
6 41 051 005103 1400000US41051005103 41051005103 51.03
NAMELSAD STUSPS NAMELSADCO STATE_NAME LSAD ALAND
1 Census Tract 16.07 OR Marion County Oregon CT 1814790
2 Census Tract 9800 OR Clackamas County Oregon CT 1634590508
3 Census Tract 13.01 OR Multnomah County Oregon CT 744911
4 Census Tract 315.16 OR Washington County Oregon CT 1879977
5 Census Tract 9505 OR Umatilla County Oregon CT 1073211610
6 Census Tract 51.03 OR Multnomah County Oregon CT 433965
AWATER geometry
1 0 MULTIPOLYGON (((-122.9903 4...
2 6684470 MULTIPOLYGON (((-122.266 45...
3 0 MULTIPOLYGON (((-122.6348 4...
4 0 MULTIPOLYGON (((-122.8068 4...
5 0 MULTIPOLYGON (((-119.4346 4...
6 155315 MULTIPOLYGON (((-122.6776 4...plot(or_tracts)
# GGPLOT
ggplot(or_tracts) +
geom_sf() +
coord_sf()
### GET CENSUS DATA
### B25077_001E: MEDIAN HOME VALUE
or <- get_acs(geography = "tract", year=this.year,
state = "OR",
variables = "B25077_001E")%>%
mutate(GEO_ID=paste0("1400000US", GEOID))
head(or)# A tibble: 6 × 6
GEOID NAME varia…¹ estim…² moe GEO_ID
<chr> <chr> <chr> <dbl> <dbl> <chr>
1 41001950100 Census Tract 9501, Baker County, Ore… B25077… 265000 46736 14000…
2 41001950200 Census Tract 9502, Baker County, Ore… B25077… 175200 40020 14000…
3 41001950300 Census Tract 9503, Baker County, Ore… B25077… 140600 22155 14000…
4 41001950400 Census Tract 9504, Baker County, Ore… B25077… 190500 36472 14000…
5 41001950500 Census Tract 9505, Baker County, Ore… B25077… 191100 11383 14000…
6 41001950600 Census Tract 9506, Baker County, Ore… B25077… 237500 46978 14000…
# … with abbreviated variable names ¹variable, ²estimate## USE GEO_JOIN TO COMBINE SPATIAL DATA AND OTHER DATA FRAMES
joinOR<- geo_join(or_tracts, or,
by_sp="GEOID", by_df="GEOID")
## USE TMAP PACKAGE
tm_shape(joinOR)+
tm_fill("estimate", style = "quantile", n=7, palette = "Greens")+
tm_legend(bg.color="white", bg.alpha=0.6)+
tm_style("gray")
## SET GEOMETRY
oregon <- get_acs(geography = "tract", year=this.year,
state = "OR",
variables = "B25077_001E",
geometry = TRUE)
#Plotting with MAPVIEW
mapview(oregon, zcol = "estimate", legend = TRUE,
lwd=.25)## Plotting with LEAFLET
## Leaflet with reactive
pal<-colorNumeric("Greens", domain=0:ceiling(max(oregon$estimate, na.rm=TRUE)))
popup<-paste("Tract: ", as.character(substring(oregon$GEOID, 6, 11)), "<br>",
"Median Home Value: ", as.character(oregon$estimate))
leaflet()%>%
addProviderTiles("CartoDB.Positron")%>%
addPolygons(data=oregon,
fillColor= ~pal(oregon$estimate),
fillOpacity = 0.7,
weight = 0.4,
smoothFactor = 0.2,
popup = popup)